In 2024, Microsoft and G42 announced a $1 billion data-centre initiative in Kenya. It later emerged that the facility would draw on nearly half the country's electricity grid - a dependency the government reportedly discovered only after the letter of intent was signed. The deal has since stalled. That fact is worth sitting with, because it's not really a story about one contract. It's a preview of a decision hundreds of African governments are about to make, at scale, over the coming years.

AI is being sold into critical public services, health records, education platforms, social protection systems, at the same moment official development aid is retreating. According to the OECD, health-sector aid to the region is projected to fall by up to 60% from its recent peak. Into that gap has stepped a wave of commercial AI investment, pitched as the natural successor to the aid that's disappearing.

The urgent question

Call that framing what it is: manufactured urgency. Not the ordinary pressure of a genuinely time-limited opportunity, but a decision environment built, deliberately, so that waiting looks more expensive than committing. 

What’s the difference?  Genuine urgency is backed by evidence specific to your context. Manufactured urgency is asserted before any evidence exists, told the same way in every market.

Familiar territory

None of this is unfamiliar to African finance ministries. The structural adjustment programmes of the 1980s and 90s were signed by governments under fiscal pressure, on terms nobody had time to properly evaluate, with consequences that outlasted the officials who signed them. By 2023 the continent's external debt had passed $1.15 trillion. AI contracts for critical services carry the same shape of risk: long-term, hard to unwind, negotiated by a handful of overstretched advisors against vendor teams built for exactly this kind of deal.

The response isn't to reject AI. Most governments shouldn't, and won't. The response is to call out who carries the burden of proof. 

Right now, a government has to justify caution. In the AI race, a government is accountable for “falling behind”. In this race, almost nothing requires the vendor to justify the impact of the technology and the legitimacy of the deal. That can be reversed. A government that simply asks: show us the real cost over ten years; show us this actually works in a context like ours; tell us honestly what water, power and land this needs. 

Necessitating that evidence stops looking like a government falling behind. Rather, when contracting at these stakes, it looks like the only defensible thing a finance ministry could do.

A way forward

That's a small shift in principle and a large one in practice and it's also, we think, an integral part of the response that's still missing. The continent already has real, capable organisations doing serious work on AI governance and safety. 

What's harder to find is the economics-backing: the cost models, the benchmarks, the evidence standards that let a government actually weigh a deal, not just govern one. 

That's why our teams are focussed on equipping decision makers with a rigorous evidence base to answer what the true costs of decisions are, what the return on investment is and what are the right questions to ask before long-term, high stakes directions are locked in. 

If this matches a gap you're seeing, or you've already tried something like it, we'd like to hear from you. Reach out directly to me, or our Frontier Technologies team - we're keen to shape this together.